SymantoResearch at SemEval-2019 task 3: combined neural models for emotion classification in human-chatbot conversations
A Basile, M Franco-Salvador, N Pawar… - Proceedings of the …, 2019 - aclanthology.org
Proceedings of the 13th international workshop on semantic evaluation, 2019•aclanthology.org
In this paper, we present our participation to the EmoContext shared task on detecting
emotions in English textual conversations between a human and a chatbot. We propose four
neural systems and combine them to further improve the results. We show that our neural
ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY)
and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best
system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.
emotions in English textual conversations between a human and a chatbot. We propose four
neural systems and combine them to further improve the results. We show that our neural
ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY)
and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best
system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.
Abstract
In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.
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